Evidence map›Paper›PMID 34691030›Full record

ArticleFrontiers in immunology2021

Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies.

Rongguo Yu, Jiayu Zhang, Youguang Zhuo, Xu Hong, Jie Ye, Susu Tang, Yiyuan Zhang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
7.7field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 55 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Rongguo YuDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China.
Jiayu ZhangSchool of Clinical Medicine, Yunnan University of Traditional Chinese Medicine, Kunming, China.
Youguang ZhuoDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China.
Xu HongDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China.
Jie YeDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China.
Susu TangDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China.
Yiyuan ZhangDepartment of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Xiamen University, Xiamen, China.
Fuzhou Second Hospital · CNXiamen University · CNYunnan University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA's diagnostic signatures and the effect of immune cell infiltration in this pathology. Methods: Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA's diagnostic signatures and further verify by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. Moreover, this study employed cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) website for assessing the inflammatory state of RA, and an investigation was conducted on the relationship of diagnostic signatures and infiltrating immune cells. Results: On the whole, 54 robust DEGs received the recognition. Lymphocyte-specific protein 1 (LSP1), Granulysin (GNLY), and Mesenchymal homobox 2 (MEOX2) (AUC = 0.955) were regarded as RA's diagnostic markers and showed their statistically significant difference by qRT-PCR. As indicated from the immune cell infiltration analysis, resting NK cells, neutrophils, activated NK cells, T cells CD8, memory B cells, and M0 macrophages may be involved in the development of RA. Additionally, all diagnostic signatures might be different degrees of correlation with immune cells. Conclusions: In conclusion, LSP1, GNLY, and MEOX2 are likely to be available in terms of diagnosing and treating RA, and the infiltration of immune cells mentioned above may critically impact RA development and occurrence.

Indexed as

Databases, GeneticAntigens, Differentiation, T-LymphocyteArthritis, RheumatoidBiomarkersComputational BiologyFemaleGene Expression RegulationHomeodomain ProteinsHumansLeukocytesMachine LearningMacrophagesMaleMast CellsMicrofilament ProteinsSynovial MembraneAntigens, Differentiation, T-LymphocyteBiomarkersGNLY protein, humanHomeodomain ProteinsLSP1 protein, humanMEOX2 protein, humanMicrofilament Proteinsbioinformatic analysisdiagnostic markerimmune cellsmachine-learning strategiesrheumatoid arthritis

Identifiers

PMID34691030
PMCPMC8526926
OpenAlexW3204022661

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.